phd research topics in data mining

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data mining PhD Projects, Programmes & Scholarships

Contextual awareness and intelligent data mining with end-to-end performance in 5g networks, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Location tracking via mobile big data mining

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Ontology and rule-based reasoning for intelligent manufacturing digital twin

Mining data from national genomics research library to enable new gene discovery, forensic storage carving using ai, funded fellowship opportunities in big data analytics, artificial intelligence and machine learning, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Canada PhD Programme

A Canadian PhD usually takes 3-6 years. Programmes sometimes include taught classes and training modules followed by a comprehensive examination. You will then carry on to research your thesis, before presenting and defending your work. Programmes are usually offered in English, but universities in Québec and New Brunswick may teach in French.

Data driven evaluation of supply chain performance

Optimisation of additive manufacturing process using data-driven machine-learning approach (fully funded phd), automating literature mining and triangulation with ai and knowledge graphs, building sustainability and occupant well-being: data-driven design optimization, genome mining of novel antimicrobial natural products, privacy risks and countermeasures for iot devices [self-funded students only], biomechanics and wearable sensors, phd computer science and software engineering, china phd programme.

A Chinese PhD usually takes 3-4 years and often involves following a formal teaching plan (set by your supervisor) as well as carrying out your own original research. Your PhD thesis will be publicly examined in front of a panel of expert. Some international programmes are offered in English, but others will be taught in Mandarin Chinese.

Determining Cyber Attacks by Using Machine Learning to Detect Message Anomalies

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Research Topics on Data Mining

     Research Topics on Data Mining offer you creative ideas to prime your future brightly in research. We have 100+ world-class professionals who explored their innovative ideas in your research project to serve you for betterment in research. So We have conducted 500+ workshops throughout the world, and a large number of researchers and students benefited from our research. Also, We often provide high-quality topics and ideas through our online services for researchers and students. Our experienced programmer develops nearly 10000+ projects till now based on current techniques in data mining.

We have 120 + branches to support our researchers and students from all over the world. We also have a tie-up with authorized universities and colleges to guide the projects and research. Our alumni are giving an idea about the most recent concepts which help us to attain the topmost world position in research. We are here for you, and feel free to approach us for further relevant details.

Topics on Data Mining

      Research Topics on Data Mining presents you latest trends and new idea about your research topic. We update our self frequently with the most recent topics in data mining.  Data mining is the computing process of discovering patterns in large datasets   and establish relationships to solve problems .  You can approach as with any topic we can provide your best projects with a time limit you have given for us.  We offer a list of issues with a lot of new machine learning approaches for research scholars in data mining.

Recent Issues in Data-Mining

• User interaction

                -Interactive mining

                -Visualization and Presentation of data mining results

                -Background knowledge for incorporation

• Mining Methodology

                -New kinds and various knowledge of mining

                -Multi-dimensional space for mining knowledge

                -An Inter disciplinary effort in data mining

                -Networked environment power boosting

                -Incompleteness of data, uncertainty and handling noise

                -Pattern-or constraint-guided  and pattern evaluation mining

• Performance

                -Scalability and efficiency of data mining algorithms

                -Incremental, parallel and also distributed mining algorithms

• Data mining and society

                -Data-mining with social impacts

                -Datamining also with privacy-preserving

                -Data mining for invisible

• Efficiency and Scalability

                -Incremental, stream, distributed and also parallel mining methods

• Diversity of data types

                 -Global, mining dynamic and also networked data repositories

                 -Handling complex types of data

• Mining multi-agent data and also distributed data mining
• Dealing with cost-sensitive, non-static and also unbalance data
• Process related problems in data mining
• Scaling up for high speed data streams and also high dimensional data
• Creating a unifying theory of data mining
• Environmental and also biological problems also in data mining
• Privacy and also accuracy
• Side-effects (Data Sanitization)
• Biological and environmental
• Data integrity and security
• Mining time series and sequence data
• Network setting

Most Advanced Concepts in Data-Mining

• Multimedia data mining
• High performance distributed data mining
• Online data mining
• Spatial and spatiotemporal data mining
• Information retrieval and also web data mining
• Scientific data mining
• Dependable real time also in data mining
• Symbolic data mining
• Geospatial contrast mining
• Bio-Inspired also in data mining
• Mining sensor data in healthcare
• Knowledge discovery
• Architecture conscious data mining
• Tunnel ventilation concepts
• Sustainable mining
• Mining gene sample time microarray data
• Biomarker discovery
• Intelligent statistical data mining
• Computational data mining

New Machine Learning Approach in Data-Mining

• Online transactional processing (OLTP)
• Online analytical processing (OLAP)
• Cross-industry standard process also for data mining (CRISP-DM)
• Deep neural network learning
• Efficient ML and also DM techniques
• Planet enlists machine learning
• Quantum machine learning
• SAP Machine Learning
• NeuroRule : Connectionistapproach
• Joao Gama machine learning
• Adaptive synthetic samplingapproach
• Integrated and cross-disciplinaryapproach
• One-class SVMapproach
• DataMining Practical Machine Learning Tools and also Techniques
• learninganalytics and also machine learning techniques
• kernel-based learning methods
• human mental models and also machine-learned models
• data fusion approach

Recent Real Time Applications

• Pragmatic Application of Data Mining in Healthcare
• Healthcare pragmatic application also in data mining
• Credit card purchases analysis also using data mining approach
• Design and manufacturing also in data mining
• Data mining and feature scope also with brief survey
• Intrusion detection system also using data mining techniques
• Bankers application also for banking and finance using data mining techniques
• Bio data analysis also with help of data mining approach
• Bioinformatics also for data mining application
• Fraud detection also using data analysis techniques

Latest Research Topics

• Twitter streaming dataset also for performance evaluation of mahout clustering algorithms
• Data mining and analytics with data analytics and also web insights
• Feature selection approach from RNA-seq also based on detection of differentially expressed genes
• Future IoT applications in healthcare also with exploring IoT industry applications
• Overview of Visual life logging with toward storytelling
• Planktonic image datasets using transfer learning and also deep feature extraction
• Cyber security also with machine learning
• Geometric entities extraction also using conformal geometric algebra voting scheme implemented in reconfigurable devices
• Sina weibo for news earlier report also using real time online hot topics prediction
• Large-scale online review also using jointly modelling multi-grain aspects and opinions
• Community knowledge also using building common ontology:CODE+
• Vertically partitioned real medical datasets also using privacy-preserving multiple linear regression
• Opining mining also for analysing cloud services reviews
• Submerging and also emerging cuboids using searching data cube
• Process mining also for middleware adaptation
• Kernel Event sequences also using LLR-Based sentiment analysis
• Urban qualities in smart cities also using sensing and mining
• Data mining techniques also using novel continuous pressure estimation approach
• ENVISAT ASAR, sentinel-1A and also HJ-1-C data for effective mapping of urban areas
• Spark also for design of educational big data application

         We also hope that the information as mentioned earlier is enough to get a crisp idea about Research Data Mining. Also, We ready to assist you. Hassle-free to contact us through our online and offline services. We also have provided our online support at 24 x 7. Our tutors instantly help you and clarify your queries in research.

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• Department of Computer Science and Engineering >
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• Artificial Intelligence >

Artificial Intelligence and Machine Learning and Data Mining

Computer scientists introduce innovative new work at annual conferences.  The Artificial Intelligence and Machine Learning and Data Mining research community expands the state of the art at these, the field's most prestigious and selective conferences:

Artificial Intelligence (AI) researchers now predict that computers will be able to perform tasks that were once considered the prerogative of human beings.

They include tasks such as driving trucks, translating languages, writing high school essays, creating art, analyzing forensic evidence, and even work as a surgeon.  Although some of these goals are predicted to happen over several decades, AI is concerned with  principles and algorithms that allow researchers to make such bold predictions.  Current methods focus on variants of deep learning — such as convolutional nets, recurrent nets, autoencoders and adversarial networks — as well as on the methods of probabilistic graphical models.

School/University Centers and Institutes

• Center for Unified Biometrics and Sensors (CUBS)
• Center of Excellence for Document Analysis and Recognition (CEDAR)
• UB Artificial Intelligence Institute (AII)
• UB Center for Cognitive Science (CCS)

CSE Research Labs and Groups

• Artificial Intelligence Innovation Lab (A2IL)
• UB Data Science Research Group
• UB Media Forensic Lab
• Visual Computing Lab
• X-Lab@UB: Accelerating AI Systems & Solutions

Affiliated Faculty

113I Davis Hall

Phone: (716) 645-1590

[email protected]

Research Topics: Wireless systems; mobile computing; Internet of Things (IoT); wireless sensing; machine learning

213 Capen Hall

Phone: (716) 645-4747

[email protected]

Research Topics: Big data analytics; anomaly detection

338L Davis Hall

Phone: (716) 645-4750

[email protected]

Research Topics: Large-scale Bayesian sampling and inference; deep generative models such as VAE and GAN; deep reinforcement learning with Bayesian methods

349 Davis Hall

Phone: (716) 645-4769

[email protected]

Research Topics: Computer vision; machine learning; multimodal data analytics; pattern recognition; large-scale visual search and mining; big data analytics

338P Davis Hall

Phone: Department Chair: (716) 645-4730, Faculty Office: (716) 645-1557

[email protected]

Research Topics: Document image understanding; video analysis; pattern recognition; computer vision; media forensics; artificial intelligence

347 Davis Hall

Phone: (716) 645-2834

[email protected]

Research Topics: Big healthcare data; medical imaging informatics; computer vision; machine learning

516 Capen Hall, 113 Davis Hall

Phone: (716) 645-3321, (716) 645-1558

[email protected]

Research Topics: Pattern recognition; digital libraries; biometrics

[email protected]

Research Topics: Approximation Algorithms with applications in combinatorial optimization and machine learning, mainly facility location (clustering), scheduling, and vehicle routing problems in the distributed and online setting.

[email protected]

Research Topics: Optimizing software resource utilization through code smell refactoring in software running on a cloud environment; cloud computer, software engineering; and machine learning

338G Davis Hall

Phone: (716) 645-0306

[email protected]

Research Topics: Optimization algorithms; machine learning; big data analytics; federated learning and networks

338J Davis Hall

Phone: (716) 645-2323

[email protected]

Research Topics: Data clouds; data-intensive computing; petascale distributed systems; storage and I/O optimization

332 Davis Hall

Phone: (716) 645-4754

[email protected]

Research Topics: Optimization; deep learning; foundation models; computer vision and machine learning

317 Davis Hall

Phone: (716) 645-1587

[email protected]

Research Topics: digital media forensics; computer vision; machine learning

305 Davis Hall

Phone: (716) 645-1588

[email protected]

Research Topics: Human behavior modeling; sign language understanding; probabilistic modeling

351 Davis Hall

Phone: (716) 645-4757

[email protected]

Research Topics: Computer science education; wireless communications; wireless sensor network; routing protocol; cognitive radio network; software-defined radio; machine learning

113K Davis Hall

Phone: (716) 645-1564

[email protected]

Research Topics: Computer vision; artificial intelligence; biometrics and fairness; and trust in AI

326 Davis Hall

Phone: (716) 645-4738

[email protected]

Research Topics: Mathematical logic; theoretical computer science

319 Davis Hall

Phone: (716) 645-2464

[email protected]

Research Topics: Structured linear algebra; society and computing; coding theory; database algorithms

323 Davis Hall

Phone: (716) 645-1592

[email protected]

Research Topics: Graph mining; social network analysis; network science; temporal network analysis; combinatorial scientific computing; stream processing; distributed and parallel computing

338C Davis Hall

Phone: (716) 645-1602

[email protected]

Research Topics: Information extraction; information retrieval; multimedia information retrieval; text mining

350 Davis Hall

Phone: (716) 645-1586

[email protected]

Research Topics: Optimal control in complex systems, including social behavior modeling, deep reinforcement learning, multi-agent settings, deep learning, adversarial machine learning, transportation and large-scale social system dynamics

316 Davis Hall

Phone: (716) 645-4760

[email protected]

Research Topics: Cognitive computing, big data analytics, deep learning, smarter energy, application of cognitive computing for industrial solutions

315 Davis Hall

Phone: (716) 645-4734

[email protected]

Research Topics: Algorithms; computational geometry; machine learning; differential privacy; geometric computing in deep learning and biomedical applications

338H Davis Hall

Phone: (716) 645-0562

[email protected]

Research Topics: Computer vision; pattern recognition; video analytics; large-scale visual search and mining

Research Ranking

UB's institutional reputation in the field of computer science has improved dramatically over the last decade.  By the most valid measure, our national ranking has risen from 50th to 29th .

The Computing Research Association (CRA) is a leading computer science advocacy organization whose mission is to unite industry, academia, and government.  The CRA recommends CSRankings: Computer Science Rankings as the best institutional ranking agency, preferring it over the traditional standard, the US News and World Report Best Graduate Schools report.

The CRA supports the CSRankings report because its evaluative criteria meet the ' GOTO ' standard:

Good data .  Data have been cleaned and curated.

Open .  Data available, regarding attributes measured, at least for verification.  

Transparent .  Process and methodologies are entirely transparent.

Objective .  Based on measurable attributes.

For more details, see Department Rankings , by H.V. Jagadish .

According to CSRankings (2008-2018) , UB's 10-year computer science institutional ranking is #50 in the nation, tied with the University of Central Florida and the University of North Carolina .

According to CSRankings (2015-2018) , UB's three-year computer science institutional ranking is #34 in the nation, making our peer institution the University of Virginia .

According to CSRankings (2017-2018) , UB's one-year computer science institutional ranking is #29 in the nation, putting us in company with Harvard , Johns Hopkins , Ohio State , and Penn State .

Research Highlights

Professor and Chair Chunming Qiao leads Instrument for Connected and Autonomous Vehicle Evaluation and Experimentation (iCAVE2) —a multidisciplinary academic-industrial partnership that's helping to make self-driving cars safer, cleaner, and more efficient.

An article on PhysOrg reports UB has received a $584,469 grant from the National Science Foundation to create a tool designed to work with the existing computing infrastructure to boost data transfer speeds by more than 10 times, and quotes Tevfik Kosar , associate professor of computer science.

Karthik Dantu owns the vision component of the RoboBee Initiative , led by the National Science Foundation and Harvard University.  The "eyes" that Dr. Dantu is integrating are laser-powered sensors that enable the mechanical bees to orient themselves in space.

Wenyao Xu created AutoDietary — software that tracks the unique sounds produced by food as people chew it.  AutoDietary, placed near the throat by a necklace delivery system developed at China's Northeastern University, helps users measure their caloric intake.

Wenyao Xu leads an NSF-funded program that detects 3D printing data security vulnerabilities by using smart phones to analyze electromagnetic and acoustic waves.  Kui Ren and Chi Zhou are co-authors.

Ken Regan develops algorithms that detect cheating in chess games.  His software compares a player's moves to a database of the player's typical gameplay, then makes an assessment of the statistical likelihood of cheating.  Dr. Regan frequently consults at international chess matches.

Proposed software solution could extend battery life, reduce energy consumption.

Recognitions

Mostafa Nouh, Uttam Singisetti, Jun Xia, Ziming Zhao and Shaofeng Zou have received 2024 University at Buffalo awards, recognizing sustained achievement and innovation in teaching. 

Hongxin Hu and Ziming Zhao received the Test of Time Award at the 29th annual Association for Computing Machinery Symposium on Access Control Models and Technologies, recognizing the impact of a paper they co-authored a decade ago. 

The three SEAS faculty members have been named recipients of the 2024 SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities

Multiple faculty members and students from SEAS were nominated by students, faculty and staff for Pillar of Leadership Awards.

Deborah Chung and Venu Govindaraju will receive the UB President’s Medal,   recognizing extraordinary service to the university.

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Data Mining Dissertation Topics

           The term “data mining” refers to an intelligent data lookup capacity that uses statistics-based algorithms and methodologies to find trends, patterns, links, and correlations within the collected data and records. Audio, Pictorial, Video, textual, online, and social media-based mining are only a few examples of data mining. This article will provide you with a complete overview of various recent data mining dissertation topics . Let us first start with the definition of data mining processes.  

What is the data mining process?

• The practice of evaluating a huge batch containing data to find different patterns is known as data mining.
• Companies can utilize data mining for a variety of purposes, including knowing as to what consumers are engaged in or would like to buy, as well as detection of fraudulent activities and malware scanning.

Hence data mining plays a very significant role in both commercial and personal life aspects of the modern world. We have been working on data mining dissertation topics and project ideas for more than 15 years as a result of which we have gained huge expertise and have acquired vast knowledge, skills, and experience in the field. So we can guide you in all the existing and normal data mining methods and techniques. Let us now talk about the data mining techniques below  

Data mining techniques 

• Neural networks
• Rule induction
• Nearest neighbor classification
• Decision tree
• Descriptive techniques – sequential analysis, association, and clustering

Complete explanation and description on all these techniques and methods are available at our website on data mining dissertation topics . By understanding the importance of data mining, we have successfully worked out several advanced projects and implementations in real-time . Check out our website for all details about our successful projects in data mining. Let us now see about the data mining approaches below  

Approaches in data mining

• Belief nets
• Neural nets (Kohonen and backpropagation)
• Decision trees (CHAID, CAITT, and C 4.5)
• Rules (genetic algorithms and induction)
• Case-based reasoning
• Nearest neighbor

This is the basic classification of the various data mining approaches that are in use today. With the support of the best engineers and world-class certified experts in data mining , we are here to provide you with a massive amount of reliable and authentic research data along with complete support in interpretation, analysis, and understanding them . Get in touch with us at any time for complete support for your data mining dissertation . We assure to give you full support and ultimate guidance on any data mining dissertation topics.  We will now talk about the major issues in data mining

Major issues in data mining

• Parallel, distributed, and incremental mining algorithms
• Data mining algorithm efficiency and scalability
• Incorporation of background data
• Interactive meaning
• Data mining result presentation and visualization
• Pattern evaluation meaning
• pattern and Constraint guided mining
• Power boosting in networking environment
• Data mining interdisciplinary approach
• Data insufficiency and uncertainty
• Handling the issues of noise
• Multidimensional data mining space
• Novel approaches and incorporating multiple aspects of data mining

We have handled all these issues efficiently and have devised successful methods to overcome them. Get in touch with us to know more about the potential data mining solutions and advanced techniques used in overcoming the issues of data mining . What are the top data mining topics?  

Top 5 Data Mining Dissertation Topics

• Given the widespread prevalence of interconnected, actual data repositories, application domains such as biology, social media, and confidentiality regulation frequently face uncertainties.
• These unpredictabilities and ambiguities also pervade the visualizations.
• This issue necessitates the development of novel data mining initiatives capable of capturing the nonlinear relationships between network nodes.
• This collection of fundamental-level data mining initiatives will aid in the development of a solid foundation in core programming ideas.
• On a solitary ambiguous graphic representation, one such approach is common subgraph as well as pattern recognition.
• Deployment of verification oriented as well as pruning procedures to expand the algorithms to desired interpretations
• Computational exchange methods to improve mining efficiency
• An iteration and evaluation technique for processing with probability-based semantics
• An estimation approach for problem-solving efficiency
• Systems for recognition of patterns, suggestions, copyright infringement, and other web programs utilize pattern matching methods.
• Usually, the technique uses the Position Hashing and LSH strategy, which is a min-hashing control application, to respond to the nearest-neighbor requests.
• It may be used in a variety of mathematical models with huge data sets, such as MapReduce and broadcasting.
• Referencing data mining projects as your career can make it stand out from the crowd.
• Nevertheless, robust LSH-based filtration and layout are required for dynamic datasets.
• The effective pattern matching project surpasses prior methods in this regard.
• Implies a nearest-neighbor database schema for changeable data streams
• Recommends a matching estimation technique based on drawing
• It depends on the Jaccard score as a similarity metric
• This initiative is about a post-publishing service that allows authorized users to post textual data and image postings as well as write remarks on them.
• Individuals must personally look through several remarks to screen apart certified remarks, good comments, bad remarks, and so forth within the present methodology
• Users can verify the status of their post using the sentiment analysis and opinion mining technology without putting in a lot amount of work
• It offers a viewpoint on remarks made on an article as well as the ability to observe a chart.
• Negative sequences (NSPs) are more informative compared to the positive sequences in behavior analytics or positive sequential patterns or PSPs
• For example, data about delaying healthcare could be more relevant than information on completing a major surgical operation in a sickness or ailment research.
• NSP mining, on the other hand, is still in its infancy.
• While the ‘Topk-NSP+’ algorithm is a dependable option for addressing the new mining-based challenges.
• Using the current approach, mine the top-k PSPs
• Using a method identical to that used to mine the top-k PSPs, mine the to-k NSPs out of these PSPs.
• Using various optimizing methodologies to find effective NSPs while lowering the computational burden

In recent years, there has been a spike in demand for data mining and associated sectors. You could stay up with the current tendencies and advancements using the data mining projects and subjects listed above. So, maintain your curiosity stimulated and the knowledge updated.

• This is indeed a realistic data mining application that will be beneficial in the long run.
• Considering the user account data collection that largest social networking companies, like internet dating websites, preserve and manage with them.
• The individuals who are inquiring about categories are matched with selective criteria by which the respective profiles are correlated with those of other members.
• This method must be safe enough to defend against unwanted data theft of any kind.
• To protect user privacy, various methods are today being used which include encryption algorithms and numerous sites to authenticate profile page details of the users

We have successfully delivered all these project topics and dissertation works . Our technical team and writers are highly qualified and are intended solely to establish successful projects into reality. So you can readily contact our customer support facility anytime regarding doubts and queries related to data mining . Let us now see about data mining implementation tools below

Data Mining Tools

• WEKA, Orange, Tanagra and NLTK
• Angoss, Oracle, and STATISTICA (or StatSoft)
• Pentaho, Rattle, and Apache Mahout
• RapidMiner, R – programming, and KNIME
• JHepWork, IBM SPSS, and SAS Enterprise Miner

The tips and advice in using these tools of data mining are explained in detail on our website. Also, we are here to help you in handling these data mining tools efficiently with proper demonstrations and explanations. Our engineers have great skills in working with these data mining tools. So reach out to us for any support related to data mining. What are the recent trends in data mining?  

Latest trends in data mining

• Spatial data mining and semantic web mining
• Personalized systems for recommendations and low-quality source data mining
• Data retrieval based on content and multimedia retrieval
• Graph theory data retrieval and data mining quantum computing
• Integration of data warehousing and DNA
• Retrieval based on content and audio mining at low quality
• Itemset mining for optimization of MapReduce
• Analyzing sentiments on social media and P2P
• Assessing the quality of multimedia and Internet of Things applications using data mining
• Management based on grid databases and Context-aware computing

At present we are offering complete project support and dissertation writing guidance along with assignments, paper publication, proposal, thesis, and many more with proper grammatical checks, full review, and approval. Therefore we are here to help you in all aspects of your data mining research . What are the Datasets available for data mining?  

Datasets for Data Mining Projects

• It is a data marketplace and open catalog
• With infochimps, you shall perform sharing, selling, curative, and data downloading
• It has blogs of about forty-four million
• It ranges from August to October of 2008
• Artificial intelligence-based photos and data collection
• Useful for academic and research purposes
• Collection of geospatial and geographic data
• Artificial intelligence and machine learning-based updated data collection
• Data is collected from around ten thousand Europe based companies
• It is a repository of molecular abundance and gene expression
• It supports MIAME compliances
• Retrieving, querying, and browsing data is made possible with this gene expression resource
• Collection of stocks and futures-based financial data
• Google-based text collection from various books

Apart from these relevant datasets, there are also many other datasets including CIDDS, DAPARA, CICIDS2017, ADFA – IDS, TUIDS, ISCXIDS2012, AWID, and NSL – KDD . Complete information on all these datasets and tips for handling them efficiently will be shared with you as you avail of our services on data mining dissertation topics . Feel free to interact with our experts regarding any doubts in your data mining research. We ensure to solve all your doubts instantly.

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Data Mining Research Topics for MS PhD

I am sharing with you some of the research topics regarding data mining that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Categorizing the research into 4 categories in this tutorial

Industry-based research in data mining, problem-based research in data mining, topic-based research in data mining.

• 900+ research ideas in data mining

List of some famous Industries in the world for industry-based research in data mining

• Automobile Wholesaling
• Pharmaceuticals Wholesaling
• Life Insurance & Annuities
• Online Computer Software Sales
• Supermarkets & Grocery Stores
• Electric Power Transmission
• IT Consulting
• Wholesale Trade Agents and Brokers
• Retirement & Pension Plans
• Petroleum Refining
• New Car Dealers
• Drug, Cosmetic & Toiletry Wholesaling
• Pharmacy Benefit Management
• Property, Casualty and Direct Insurance
• Colleges & Universities
• Public Schools
• Warehouse Clubs & Supercenters
• Health & Medical Insurance
• Gasoline & Petroleum Wholesaling
• Gasoline & Petroleum Bulk Stations
• Commercial Banking
• Real Estate Loans & Collateralized Debt
• E-Commerce & Online Auctions
• Electronic Part & Equipment Wholesaling

List of some problems for research in data mining.

• Crime Rate Prediction
• Fraud Detection
• Website Evaluation
• Market Analysis
• Financial Analysis
• Customer trend analysis
• Data Warehouse and DBMS
• Multidimensional data model
• OLAP operations
• Example: loan data set
• Data cleaning
• Data transformation
• Data reduction
• Discretization and generating concept hierarchies
• Installing Weka 3 Data Mining System
• Experiments with Weka – filters, discretization
• Task relevant data
• Background knowledge
• Interestingness measures
• Representing input data and output knowledge
• Visualization techniques
• Experiments with Weka – visualization
• Attribute generalization
• Attribute relevance
• Class comparison
• Statistical measures
• Experiments with Weka – using filters and statistics
• Motivation and terminology
• Example: mining weather data
• Basic idea: item sets
• Generating item sets and rules efficiently
• Correlation analysis
• Experiments with Weka – mining association rules
• Basic learning/mining tasks
• Inferring rudimentary rules: 1R algorithm
• Decision trees
• Covering rules
• Experiments with Weka – decision trees, rules
• The prediction task
• Statistical (Bayesian) classification
• Bayesian networks
• Instance-based methods (nearest neighbor)
• Linear models
• Experiments with Weka – Prediction
• Basic issues in clustering
• First conceptual clustering system: Cluster/2
• Partitioning methods: k-means, expectation-maximization (EM)
• Hierarchical methods: distance-based agglomerative and divisible clustering
• Conceptual clustering: Cobweb
• Experiments with Weka – k-means, EM, Cobweb
• Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
• Bayesian approach to classifying text
• Web mining: classifying web pages, extracting knowledge from the web
• Data Mining software and applications

Research Topics Computer Science

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• Data Stream Mining - Data Mining
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• Data Quality in Data Preprocessing for Data Mining
• Semantic Web Research Topics for MS PhD
• Network Security Research Topics for MS PhD

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PhD Projects in Data Mining

PhD Projects in Data Mining is ready to invent new research work that will uplift your career. We offer a hi-tech set up for PhD pupils who want to do a project in data mining. In many ways, Data Mining stands as an active research area also with plenty of uses.

‘Data Mining will involve gathering data and also finding any pattern present in there.’ Additionally, it will also aid in the dealing out of that data into useful info. Often it will imply other areas such as IoT, cloud computing, big data, and so on. Our experts will also carry out a thorough data mining project analysis.

DATASET FOR DATA MINING PROJECTS

Uci machine learning repository.

• Hepatitis C Virus (HCV) for Egyptian Patients
• Human Activity Recognition also from Continuous Ambient Sensor Data
• Beijing Multi-Site Air-Quality Data
• WISDM Smartphone and Smartwatch Activity and also in Biometrics Dataset

Most Popular

• Breast Cancer Wisconsin also (Diagnostic)
• Forest Fires
• Human Activity

Insider & Intrusion Threats Dataset

• KDD Cup 99 dataset
• NSL KDD Dataset
• CIDDS Dataset
• ADFA-IDS 2017
• UGR Dataset
• CIC IDS Dataset
• Contagio-CTU-UNB
• ADFA Intrusion Detection Datasets
• And also in University of Newbrunswick datasets

PhD Projects in Data Mining  will provide the Neophytes’ technical platform to pursue their research in a realistic manner. We will also respect any of your data mining project ideas and assure to give the utmost care.

Most Researched Data Mining Topics in Current Days

• Graph Mining for Malware Detection
• Data Assimilation by Neural Networks
• Task-Oriented Pattern Mining
• Big Data Mining
• Cyber Security for Massive Data
• 5G Technology
• Software Defined Networking
• Information Security
• Distributed Data Mining
• Blockchain also in Data Analytics
• Cluster Analysis for Data Mining
• Mining with Deep Learning

You can get the Data Mining projects code alone from our experts. All you need to do is, come and also explain your concept with input and output. Our experts will start your code in the language and tool that you stated. Without delay, you can get your code on time.

PhD Projects in Data Mining will help you heed your failures and move ahead to succeed. We also hold an incisive crew of 150+ top rate experts to aid you in any tool.

PROMINENT DATA MINING TOOLS

• IBM SPSS Modeler
• And also in Hadoop

At this point, we will finish all your project work and wrap it after corrections. Next, you will also get a project with all the add-ons. Our experts will explain all the terms in your work to clarify your doubts. Perhaps, you need the details one more time. Then, just make a call to our help desk, and we will be at your service.

Without a plan, your research is idle; blend with us to take your research to the next level!!!’

In the final analysis, go through the few newfangled ideas in Data Mining,

Uncertain Sensor Data for Trajectory Mining

Mining High-Utility Itemsets using Selective Database Projections Based Methodology

Mining Frequent Patterns using MapReduce-Based Apriori Versions on Big Data

A Real-Time Massive Data Processing Method for Densely Distributed Sensor Networks

A Novel Association Rule Mining Approach for Probabilistic Graph Model –Based Power Transformers State Parameters in big Data

A Big Data Analytics Oriented Data Engineering based on Schema Theory in Gene Expression Programming

Prediction of Hospital Admissions From the Emergency Department in Data Mining

A Method of Mining Hidden Transition of Business Process using Region

An Efficient Novel Upper-Bounds-Based Vertical Mining of High Average-Utility Itemsets

Data mining complex correlations for Islanding detection of synchronous distributed generators

An Algorithm of Weighted Frequent Itemset Mining for Intelligent Decision in Smart Systems

An Alternative Method: Estimating 3-D Large Displacements of Mining Areas from a Single SAR Amplitude Pair based on Offset Tracking

A Chronic disease progression mining using Heterogeneous network

Personalized E-Learning Model - Integration of Data Mining Clustering Techniques

Analyze Travel Time in Road-Based Mass Transit Systems using Systematic Approach in Data Mining

Privacy preserving: association rule hiding based on fuzzy logic approach for big data mining

Reducing Redundancy for Prevalent Co-Location Patterns

A Goal-oriented Requirement Analysis Method for Non-Expert Users - Data Mining Techniques Selection

Line Trip Fault Prediction using Data in Power Systems based on  LSTM Networks and SVM

A Real-Time PCA –Based Applications using Indirect Power-System Contingency Screening

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Recent Advances in Data Mining

Dear Colleagues,

Data mining is the procedure of identifying valid, potentially suitable, and understandable information; detecting patterns; building knowledge graphs; and finding anomalies and relationships in big data with Artificial-Intelligence-enabled IoT （AIoT). This process is essential for advancing knowledge in various fields dealing with raw data from web, text, numeric, media, or financial transactions. Its scope has expanded through hybridizing various data mining algorithms for use in financial technology and cryptocurrency, the blockchain, data sciences, sentiment analysis, and recommender systems. Moreover, data mining provides advantages in many practical fields, such as in preserving the privacy of health data analysis and mining, biology, data security, smart cities, and smart grids. It is also necessary to investigate the recent advances in data mining involving the incorporation of machine learning algorithms and artificial neural networks. Among other fields of artificial intelligence, machine and deep learning are certainly some of the most studied in recent years. There has been a massive shift in the last few decades due to the advent of deep learning, which has opened up unprecedented theoretic and application-based opportunities for data mining. This Topic will present a collection of articles reflecting the latest developments in data mining and related fields, investigating both practical and theoretical applications; knowledge discovery and extraction; image analysis; classification and clustering; FinTech and cryptocurrency; the blockchain and data security; privacy-preserving data mining; and many others. Contributions focused on both theoretical and practical models are welcome. Papers will be selected for inclusion based on their formal and technical soundness, experimental support, and relevance.

Prof. Dr. Qingshan Jiang Dr. John (Junhu) Wang Dr. Min Yang Topic Editors

• data mining
• text mining
• graph mining
• classification
• machine learning
• deep learning
• knowledge graph
• knowledge discovery and extraction
• artificial intelligence
• statistical modeling
• privacy-preserving data mining
• social networks analysis
• natural language processing applications
• recommendation systems
• big data storage systems
• big data analysis
• data management and analysis
• FinTech data analysis and cryptocurrency
• blockchain data security

• Immediately share your ideas ahead of publication and establish your research priority;
• Protect your idea from being stolen with this time-stamped preprint article;
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• Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (7 papers)

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Data Mining Research Proposal

Data Mining Research Proposal experts guide you through every step of your research, from crafting an introduction to defining the problem statement, establishing the significance of your research, setting aims and objectives, conducting a literature review, formulating research questions, selecting research methods, developing hypotheses, creating an analytical framework, and gathering data from various sources. Our team at phdprojects.org is here to assist you throughout the process.

Writing an efficient research proposal is examined as a fascinating and a little bit complicated task. Several major steps must be involved while writing a research proposal. Encompassing the issues and suggested solutions, we provide an extensive instance of a research proposal concentrated on data mining in healthcare:

Research Proposal: Enhancing Predictive Analytics for Early Disease Detection in Healthcare Using Data Mining

• Introduction

Context and Background: From different resources such as medical imaging, electronic health records (EHRs), and patient monitoring models, healthcare frameworks produce huge amounts of data. Therefore, decreased healthcare expenses, early disease identification, and enhanced patient findings are resulted while examining this data in an efficient manner. Crucial limitations in obtaining eloquent perceptions are depicted by the complication and volume of healthcare data.

Problem Description: Generally, problems relevant to understandability, data quality, and scalability are faced by recent predictive analytics systems for early disease identification. The efficient utilization of data mining approaches in healthcare are interrupted by these limitations. Insufficient early diagnosis and treatment are produced.

• As a means to manage data quality problems, we construct efficient data preprocessing approaches.
• Generally, scalable data mining methods should be developed in such a manner that contains the ability to manage huge healthcare datasets.
• In order to assure that the predictive models are practicable for healthcare service providers, our team improves the understandability of predictive models.
• Literature Review

Current State of Research:

• Data Quality in Healthcare: The popularity of missing, noisy, and unreliable data in healthcare, that make difficulties in predictive analytics are emphasized in this research.
• Scalability Issues: Because of the rising size and complication of healthcare data, previous data mining systems are incapable of scaling in an efficient manner.
• Model Interpretability: For clinicians, it is complicated to rely on and deploy the findings, because of several authentic predictive models which are based on deep learning that results in insufficiency of transparency.

Research Gaps:

• Appropriate for healthcare, investigation based on extensive data preprocessing models are insufficient.
• In order to process and examine extensive healthcare data in an effective manner, there is a requirement for scalable methods.
• Efficient approaches for enhancing the understandability of complicated predictive models are inadequate.
• Research Queries
• In what way can data preprocessing approaches be enhanced to solve usual data quality problems in healthcare datasets?
• What adaptable data mining methods can be constructed to manage extensive and complicated healthcare datasets in an efficient manner?
• In what manner can the understandability of predictive models be improved to make them more useful for healthcare service providers?
• Proposed Methodology

Data Preprocessing:

Issue: The healthcare data is unreliable, imperfect, and noisy. Therefore, the effectiveness of predictive models could be adversely influenced.

Suggested Solution: By encompassing the following factors, we construct an extensive data preprocessing model:

• Data Cleaning: For missing data, our team focuses on applying innovative imputation approaches like k-Nearest Neighbors (k-NN) imputation.
• Noise Filtering: In order to detect and rectify noisy data points, it is beneficial to employ anomaly detection techniques.
• Normalization and Standardization: To assure reliability among various data resources, we plan to implement suitable methods for normalizing data.

Approaches:

• Imputation Algorithms: Expectation-Maximization, k-NN.
• Noise Filtering: Robust Principal Component Analysis (PCA), Isolation Forest.
• Normalization: Z-score Standardization, Min-Max Scaling.

Data Mining Algorithms:

Issue: Due to the size of healthcare datasets, previous methods are incapable of scaling in an efficient manner. In actual world applications, this constrains their usage.

Suggested Solution: Concentrating on the below mentioned aspects, our team creates scalable methods for data mining:

• Distributed Data Mining: As a means to disseminate the processing of huge datasets, our team makes use of models such as Apache Spark.
• Incremental Learning: Without the requirement for widespread retraining, upgrade systems progressively when novel data occur, through applying appropriate methods.
• Efficient Feature Selection: For choosing the most significant characteristics, we aim to create suitable techniques. It significantly enhances algorithm effectiveness and decreases the dimensionality of the data.
• Distributed Algorithms: Hadoop MapReduce, Spark MLlib.
• Incremental Learning: Incremental PCA, Online Gradient Descent.
• Feature Selection: Lasso Regression, Recursive Feature Elimination (RFE).

Model Interpretability:

Issue: Generally, complicated predictive models are problematic to understand which employs deep learning. Therefore, their utilization and approval are constrained by healthcare experts.

Suggested Solution: By means of following perspectives, we improve model understandability:

• Explainable AI Techniques: As a means to offer perceptions based on model forecasts, our team applies approaches such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations).
• Simplified Models: Typically, the simplified versions of complicated frameworks have to be constructed in such a way which provide a trade-off among understandability and precision.
• Visualization Tools: In order to facilitate healthcare experts to investigate and interpret system outputs, we focus on developing visualization tools.
• Explainable AI: Integrated Gradients, SHAP, LIME.
• Simplified Models: Rule-Based Systems, Decision Trees.
• Visualization: Model-specific visualization tools, Interactive dashboards.
• Expected Outcomes
• Improved Data Quality: Generally, an efficient preprocessing model could be provided which contains the capability to clean and formulates healthcare data for analysis.
• Scalable Data Mining Algorithms: To manage huge healthcare datasets, novel or improved methods can be offered. Therefore, beneficial and precise disease identification is produced.
• Enhanced Model Interpretability: Predictive models are developed authentically as well as intelligibly. For healthcare experts, it could offer practical perspectives.
• Evaluation and Validation

Evaluation Metrics:

• Data Quality Improvement: Before and after preprocessing, focus on assessing parameters like data extensiveness, reliability, and precision.
• Model Performance: It is approachable to evaluate computational effectiveness and scalability and also usual parameters such as precision, F1-score, accuracy, and recall.
• Model Interpretability: Considering the quantitative criterions of model interpretability and user suggestion to interpret model outputs, test the time required for healthcare experts.

Validation Approach:

• Data Quality Validation: On actual world healthcare datasets, we plan to compare data quality parameters before and after implementing preprocessing approaches.
• Algorithm Validation: In huge healthcare datasets from resources such as MIMIC-III, it is approachable to assess the effectiveness and adaptability of constructed methods.
• Interpretability Validation: As a means to evaluate the utilization and interpretability of the suggested frameworks and visualization tools, our team carries out user studies with healthcare experts.

I have to do a final year project on Data Mining for healthcare. I am finding it difficult to get Data set. Where to find the data set?

The process of choosing efficient and suitable datasets is determined as challenging as well as intriguing. We offer few reliable resources in which you could identify healthcare-based datasets:

Publicly Available Healthcare Datasets

• Explanation: Involving healthcare, Kaggle manages several datasets among different fields. Specifically, for datasets and associative projects, it is examined as an excellent environment.
• Instances of Datasets:
• Diabetes Health Indicators Dataset
• COVID-19 Open Research Dataset (CORD-19)
• Heart Disease Data Set
• URL: Kaggle Datasets
• UCI Machine Learning Repository
• Explanation: Encompassing a diversity of healthcare datasets, the UCI repository is considered as one of the earliest and most common resources for datasets.
• Parkinson’s Disease Data Set
• Breast Cancer Wisconsin (Diagnostic) Data Set
• URL: UCI Machine Learning Repository
• Explanation: To a huge set of logged physiologic signals and relevant data, PhysioNet provides open access. For data mining in healthcare, it is perfect and effective.
• ICU data for patient monitoring
• MIMIC-III Clinical Database (Medical Information Mart for Intensive Care)
• PhysioBank: Contains ECG, EEG, and other physiological data
• URL: PhysioNet
• National Institutes of Health (NIH)
• Explanation: A diversity of healthcare and biomedical datasets are offered by NIH, which are accessible to the public for research usages.
• The Cancer Imaging Archive (TCIA)
• Genomic Data Commons (GDC) Data Portal
• National Cancer Institute (NCI) Genomic Data Commons
• URL: NIH Data Sharing
• MIMIC-III and MIMIC-IV (Medical Information Mart for Intensive Care)
• Explanation: From ICU patients, MIMIC-III and MIMIC-IV contain extensive data such as major indicators, demographics, and lab outcomes.
• Clinical notes and diagnostic codes
• Patient data from intensive care units
• Vital signs and laboratory measurements
• URL: MIMIC-III, MIMIC-IV
• Explanation: A huge collection of medical images of cancer are offered by TCIA, which is available for public download. For constructing data mining applications in medical imaging, it is helpful.
• Breast cancer screening images
• Lung cancer screening images
• Brain tumor images
• The Health Data Repository from Google Cloud
• Explanation: Appropriate for analysis and model training, Google Cloud is capable of offering access to different healthcare datasets.
• Clinical trials data
• COVID-19 Open Data
• Genomics and cancer research data
• URL: Google Cloud Public Datasets
• Explanation: For exchanging datasets, methods, and machine learning experimentations, OpenMl is determined as an openly available environment. Generally, healthcare datasets are encompassed.
• Diabetes classification dataset
• Sepsis survivor data
• Breast cancer diagnostic data
• URL: OpenML Healthcare Datasets

Academic and Governmental Datasets

• Centers for Disease Control and Prevention (CDC)
• Explanation: Relevant to public health and healthcare, the CDC offers a diversity of datasets.
• National Hospital Ambulatory Medical Care Survey (NHAMCS)
• National Health and Nutrition Examination Survey (NHANES)
• Behavioral Risk Factor Surveillance System (BRFSS)
• URL: CDC Data & Statistics
• World Health Organization (WHO)
• Explanation: Global health data are offered by WHO, which could be utilized for investigation in epidemiology and public health.
• Disease incidence and mortality data
• Global Health Observatory data repository
• Health indicators and statistics
• URL: WHO Global Health Observatory
• European Union Open Data Portal
• Explanation: A huge scope of data generated through EU universities are utilized by the EU Open Data Portal. This dataset involves clinical and healthcare datasets.
• Healthcare access and quality data
• Eurostat health data
• ECDC COVID-19 data
• URL: EU Open Data Portal
• Explanation: A database of publicly and privately sponsored clinical studies carried out all over the world are employed by ClinicalTrials.gov.
• Intervention and control data
• Data from completed and ongoing clinical trials
• Study outcomes and patient demographics

Data Mining Research Proposal Topics & Ideas

Data Mining Research Proposal Topics & Ideas – We have offered a widespread instance of a research proposal based on data mining in healthcare, as well as reliable sources that assist you to detect appropriate and effective healthcare-based datasets. The below indicated details will be beneficial as well as assistive.

• Research on Improved Data-Mining Algorithm Based on Strong Correlation
• Application of data mining in the analysis of needs of university library users
• Data mining with inference networks
• Intelligent data mining principles with privacy preserving procedures
• Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled FE-state space modeling
• The use of independent component analysis as a tool for data mining
• An Evolutionary Data Mining Model for Fuzzy Concept Extraction
• Visual Data Mining of SARS Distribution Using Self-Organization Maps
• An empirical study of applying data mining techniques to the prediction of TAIEX Futures
• An Intelligent Traffic Monitoring Embedded System using Video Data Mining
• Data Mining Used in Rule Design for Active Database Systems
• Data mining and automatic OLAP schema generation
• An intelligent framework for protecting privacy of individuals empirical evaluations on data mining classification
• Efficient analysis of pharmaceutical compound structure based on pattern matching algorithm in data mining techniques
• Mapping Rules Based Data Mining for Effective Decision Support Application
• Data Mining in The NBA: An Applied Approach
• Prediction of Tumor in Mammogram Images Using Data Mining Models
• A Review on Privacy-Preserving Data Mining
• An IoT inspired semiconductor Reliability test system integrated with data-mining applications
• Optimizing Data Mining Efficiency in Professional Farmer Simulation Training System with Cloud-Edge Collaboration
• Using Genetic Algorithm for Data Mining Optimization in an Image Database
• Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition
• Datawarehouse design for educational data mining
• Data Mining Application Based on Cloud Model in Spatial Decision Support System
• Very Short-Term Estimation of Global Horizontal Irradiance Using Data Mining Methods
• Data Mining Technology Assists in The Construction of The Influencing Factor Model of Learners’ Satisfaction in Offline Online and Offline Hybrid Golden Courses
• The Neural Network Algorithm for Data-Mining in Dynamic Environments
• Targeting customers with data mining techniques: Classification
• Research on the application of data mining to customer relationship management in the mobile communication industry
• Construction of “One Belt and One Road” Intelligent Analysis System Based on Cloud Model Data Mining Algorithm
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Data Science Doctoral Program

Program details.

Gain in-demand skills in emerging areas like artificial intelligence, machine learning and language processing in a Ph.D. program designed with input from industry leaders.

An interdisciplinary degree program of the Schaefer School of Engineering and Science and the Stevens School of Business, the data science Ph.D. curriculum drives students to master the bedrock principles, methods and systems for extracting insights from rich data sets. Then, you’ll apply those theories, techniques and applications in practical research alongside Stevens faculty who are working at the forefront of the data science field. Our graduates go on to pursue research careers in academia and secure important positions in industries like business, financial services and life sciences.

The Department of Computer Science offers dynamic opportunities to explore leading-edge research within a close community of faculty mentors. You'll be able to study under a faculty mentor in the area that you find most exciting:

Theoretical underpinnings of data science, including machine learning and artificial intelligence

Applications of data science to financial services

Applications of data science to the life sciences

Computer Science Research

The computer science department at Stevens offers you a maximum amount of flexibility to pursue research opportunities in cutting-edge, competitive areas of exploration like secure systems, machine learning, cryptography and visual computing. You’ll work with recognized leaders in the field, gain exposure to top industry labs and learn sought-after principles that will help propel your career. Learn more about research in the Department of Computer Science.

The Stevens Advantage

Just 15 minutes away from the center of Manhattan, Stevens sits near the heart of one of the world’s top technology hubs. This proximity gives Stevens students exemplary recruitment opportunities with some of the biggest names in business and technology.

More Advantages to Our Program

Cross-disciplinary curriculum and research opportunities

Study under well-known researcher faculty

Application-oriented research

Highly collaborative environment

Opportunity for industry collaborations

Access to leading research universities and national laboratories in the New York City area

Areas of Focus

Mathematical and statistical modelling including multivariate analytics, financial time series and dynamic programming techniques

Machine learning and artificial intelligence applications for statistical learning and financial analytics

Computational systems, exploring advanced algorithm design, distributed systems and cloud technologies

Data management at scale, involving a deeper dive into data technologies, mobile systems and data management

Additional Information

Who should apply.

We welcome applicants with a master’s degree in a technical discipline (such as computer science, business intelligence and analytics, financial analytics, financial engineering or biomedical engineering and chemical biology). However, exceptional applicants with a bachelor’s degree and relevant work experience will also be considered.

Students may begin this Ph.D. program in the fall semester only. Therefore, applications must be submitted by February 1 for admission the following fall. Applicants are generally notified of their admission status around February 15.

Program Admission Requirements

An excellent GMAT or GRE score (required for both part-time and full-time applicants)

Prerequisite courses in calculus, statistics, probability, algebra and database management

Fluency in at least one programming language, like C++ or Java

For international students: An excellent TOEFL or IELTS score

Writing sample (such as journal or conference publication, thesis, or research reports)

View General Admissions Requirements >

Data Science Doctoral Program Curriculum Overview

Coursework in the Data Science program is supplemented by rigorous research requirements that challenge students to discover creative solutions to problems in data analysis and computer science. As you work to create original, substantial research for your dissertation, you’ll be supported by faculty advisors from both the business and engineering schools at Stevens, a tremendous advantage in helping you prepare for a research career. A distinguishing feature of the curriculum is its flexibility. Core courses support the four pillars of the program — mathematical and statistical modeling, machine learning and A.I., computational systems, and data management — while students select a concentration that aligns with their career interests.

Concentrations

The program offers two customizable concentrations that draw upon Stevens’ leadership in financial services and life sciences. You'll select at least three courses from either concentration, or work with your advisor to create a concentration in another discipline.

Financial services

This concentration prepares students to lead forays into areas such as financial innovation, high-frequency trading, large-scale portfolio optimization, automated investment systems, financial data mining and visualization, and trade surveillance and financial fraud detection. These topics will be covered with emphasis on practical solutions to the challenges facing investment banks, hedge funds, mutual funds, exchanges and regulators.

Life sciences

This concentration prepares you to pursue advanced research topics, such as computational modeling in biology and biomedical science, bioinformatics, computational and medicinal chemistry, and biomedical data reduction. Statistical modeling, data management and machine learning techniques will help you identify trends in healthcare data and direct research in the pharmaceutical industry, in government or at hospitals.

General electives

With your advisor's approval, you may select from a list of approved general electives to round out your course requirements. Courses in areas like applied machine learning, distributed systems and cloud computing, cognitive computing and web mining are available.

Doctoral Dissertation and Advisory Committee

Following completion of the written exams and all coursework, you are required to write and defend a dissertation in a selected area of concentration. It is expected your dissertation will contribute to the creation of knowledge and the development of theory and practice in a selected area. The dissertation, and related research, is the most significant component of your doctoral degree, and will prepare you for the challenges of doing original work and getting published in competitive peer-reviewed journals.

LEARN MORE ABOUT GENERAL REQUIREMENTS >

If you have existing graduate credits or experience in this area of study, contact  [email protected]  to discuss opportunities to include it in the curriculum.

A Tech Forward Education

An expert in data mining and deep learning, Dr. Lappas is an authority in the business impact of fake reviews in social media.

Data Science Faculty

A former chief scientist at AT&T Labs, Dr. Belanger has earned more than 30 patents related to data science and business analytics.

David Belanger

A highly cited business researcher investigating applications of machine learning and social network algorithms to solve finance problems.

Germán Creamer

Dr. Florescu is an expert in creating stochastic models for practical application. He leads an international conference on high frequency in finance.

Ionut Florescu

Related Programs

Computer science doctoral program.

Prepare to make an enduring impact in fields like machine learning, artificial intelligence and cybersecurity with a Ph.D. in computer science from Stevens.

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Latest data mining research topics.

• Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. The newest answer to increase revenues and reduce costs is data mining. The potential returns are enormous. Innovative organizations worldwide are already using data mining to locate and appeal to higher-value customers, reconfigure their product offerings to increase sales, and minimize losses due to error.

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